1) End-to-end model vs. hybrid model. End-to-end learning models are able to predict self-motion and scene directly from raw data, without any hand-engineering. Benefited from the advances of deep learning, end-to-end models are evolving fast to achieve increasing performance in accuracy, efficiency and robustness. Meanwhile, these models have been shown easier to be integrated with other high-level learning tasks, e.g. path planning and navigation[1]}. Fundamentally, there exist underlying physical or geometric models to govern localization and mapping systems. Whether we should develop end-to-end models relying only on the power of data-driven approaches or integrate deep learning modules into the pre-built physical/geometric models as a hybrid model is a critical question for future research. As we can see, hybrid models already achieved the state-of-the art results in many tasks, e.g. visual odometry[2]}, and global localization[3]}.
Thus, it is reasonable to investigate in the way to take better advantage of the prior empirical knowledge from deep learning for hybrid models.
On the other side, pure end-to-end models are data hunger. The performance of current models can be limited by the size of training dataset, and it is essential to create large and diverse dataset to enlarge the capacity of data-driven models.
